Apparatus to Facilitate the Administration of a Knowledge-Based Radiation Treatment Plan

ABSTRACT

A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

CROSS-REFERENCE TO RELATED APPLICATION

This is a continuation of U.S. patent application Ser. No. 16/926,990,filed Jul. 13, 2020, which is a continuation of U.S. patent applicationSer. No. 15/662,799, filed Jul. 28, 2017, now U.S. Pat. No. 10,744,342,which are all hereby incorporated herein by reference in their entirety.

TECHNICAL FIELD

These teachings relate generally to the use of radiation as atherapeutic treatment and more specifically to the formation and use ofcorresponding radiation-treatment plans.

BACKGROUND

The use of radiation to treat medical conditions comprises a known areaof prior art endeavor. For example, radiation therapy comprises animportant component of many treatment plans for reducing or eliminatingunwanted tumors. Unfortunately, applied radiation does not inherentlydiscriminate between unwanted areas and adjacent healthy tissues,organs, or the like that are desired or even critical to continuedsurvival of the patient. As a result, radiation is ordinarily applied ina carefully administered manner to at least attempt to restrict theradiation to a given target volume.

Treatment plans typically serve to specify any number of operatingparameters as pertain to the administration of such treatment withrespect to a given patient. Such treatment plans are often optimizedprior to use. (As used herein, “optimization” will be understood torefer to improving upon a candidate treatment plan without necessarilyensuring that the optimized result is, in fact, the singular bestsolution.) Many optimization approaches use an automated incrementalmethodology where various optimization results are calculated and testedin turn using a variety of automatically-modified (i.e., “incremented”)treatment plan optimization parameters.

Treatment plan optimization techniques, such as so-called inverseplanning, typically requires the setting of optimization objectives.Generally speaking, setting optimization objectives has traditionallyrelied upon experienced users. Such persons have often learned, throughpractice, that “easy” objective can lead to a sub-optimal plan while“hard” objective can lead to sub-optimal trade-offs. And evenexperienced users often find themselves tethered to an iterative processthat is frequently demanding of repeated interaction in these regards.

BRIEF DESCRIPTION OF THE DRAWINGS

The above needs are at least partially met through provision of theapparatus to facilitate the administration of a knowledge-basedradiation treatment plan described in the following detaileddescription, particularly when studied in conjunction with the drawings,wherein:

FIG. 1 comprises a block diagram as configured in accordance withvarious embodiments of these teachings;

FIG. 2 comprises a flow diagram as configured in accordance with variousembodiments of these teachings;

FIG. 3 comprises a flow diagram as configured in accordance with variousembodiments of these teachings;

FIG. 4 comprises graphs as configured in accordance with variousembodiments of these teachings; and

FIG. 5 comprises a flow diagram as configured in accordance with variousembodiments of these teachings.

Elements in the figures are illustrated for simplicity and clarity andhave not necessarily been drawn to scale. For example, the dimensionsand/or relative positioning of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of various embodiments of the present teachings. Also,common but well-understood elements that are useful or necessary in acommercially feasible embodiment are often not depicted in order tofacilitate a less obstructed view of these various embodiments of thepresent teachings. Certain actions and/or steps may be described ordepicted in a particular order of occurrence while those skilled in theart will understand that such specificity with respect to sequence isnot actually required. The terms and expressions used herein have theordinary technical meaning as is accorded to such terms and expressionsby persons skilled in the technical field as set forth above exceptwhere different specific meanings have otherwise been set forth herein.

DETAILED DESCRIPTION

Generally speaking, pursuant to these various embodiments a controlcircuit accesses information regarding a plurality of pre-existingvetted radiation treatment plans for a variety of patients and uses thatinformation to train at least one model. The control circuit then usesthat model to develop estimates for a radiation treatment plan for aparticular patient. The control circuit can then use those estimates todevelop a candidate radiation treatment plan.

By one approach, the aforementioned information regarding the pluralityof pre-existing vetted radiation treatment plans for a variety ofpatients constitutes an abridged version of such plans such that theinformation is anonymous and not intrinsically correlated to any of thepatients. In a typical application setting the source pre-existingvetted radiation treatment plans may have differing original formatsfrom one to the next. In such a case, if desired, these teachings willaccommodate presenting the aforementioned information to the controlcircuit using a single consistent format.

By one approach the control circuit uses the aforementioned informationto train a model by, at least in part, identifying outlier data in theinformation and avoiding relying upon such outlier data when trainingthe model. By another approach, in lieu of the foregoing or incombination therewith, when training the model the control circuit canuse original prescription dose levels as correspond to at least some ofthe pre-existing vetted radiation treatment plans (for example, by usingthe original prescription dose levels when normalizing dose volumehistograms).

When using the aforementioned model to develop estimates for a radiationtreatment plan for a particular patient, by one approach the controlcircuit uses metadata corresponding to the source information to selectsuitable content with which to train the model.

By one approach the control circuit uses geometric limits correspondingto the aforementioned information to assess how well a particularpatient fits the information used to train the model. When theparticular patient does not fit the information used to train the modelwithin at least a predetermined range of suitability, these teachingswill accommodate presenting a corresponding warning to the user.

These teachings will also accommodate having the control circuitdetermine at least one radiation treatment plan objective for use whenoptimizing a resultant radiation treatment plan for a particular patientby forming one or more radiation treatment plan objectives based upondose volume histogram estimates that the control circuit formed usingthe aforementioned model. If desired, these teachings will accommodateproviding a user opportunity to permit the user to modify one or moresuch automatically determined radiation treatment plan objectives.

These teachings are highly flexible in practice and will accommodate awide variety of modifications and additional activity. As one example inthese regards, the control circuit can be configured to determine avalidation status of the aforementioned model and inhibit availabilityof that model when the validation status has other than a predeterminedvalue/level. This inhibited availability may comprise, for example,preventing publication of the corresponding model to a wider audience.

So configured, these teachings provide a system that can be used, forexample, to collect a set of pre-existing and previously-utilizedtreatment plans and to generate one or more dose estimation models atleast partially (or fully) based thereon. This system can furtherestimate DVH's based on such a model and facilitate generatingobjectives based on those DVH's. This system can also facilitate sharingthe estimation and objective model with other users including users ofother institutions/clinics. In all of the foregoing sharingopportunities the source information derived from actual patients can behighly anonymized to protect patient privacy while also reducing anycorresponding computational processing loading.

These and other benefits may become clearer upon making a thoroughreview and study of the following detailed description. For the sake ofan illustrative example it will be presumed for the purpose of thefollowing description that a control circuit of choice carries out someor all of the described activities. FIG. 1 presents one example of anenabling apparatus 100 in these regards.

In this particular example, the enabling apparatus 100 includes such acontrol circuit 101. Being a “circuit,” the control circuit 101therefore comprises structure that includes at least one (and typicallymany) electrically-conductive paths (such as paths comprised of aconductive metal such as copper or silver) that convey electricity in anordered manner, which path(s) will also typically include correspondingelectrical components (both passive (such as resistors and capacitors)and active (such as any of a variety of semiconductor-based devices) asappropriate) to permit the circuit to effect the control aspect of theseteachings.

Such a control circuit 101 can comprise a fixed-purpose hard-wiredhardware platform (including but not limited to an application-specificintegrated circuit (ASIC) (which is an integrated circuit that iscustomized by design for a particular use, rather than intended forgeneral-purpose use), a field-programmable gate array (FPGA), and thelike) or can comprise a partially or wholly-programmable hardwareplatform (including but not limited to microcontrollers,microprocessors, and the like). These architectural options for suchstructures are well known and understood in the art and require nofurther description here. This control circuit 101 is configured (forexample, by using corresponding programming as will be well understoodby those skilled in the art) to carry out one or more of the steps,actions, and/or functions described herein.

The control circuit 101 operably couples to a memory 102. This memory102 may be integral to the control circuit 101 or can be physicallydiscrete (in whole or in part) from the control circuit 101 as desired.This memory 102 can also be local with respect to the control circuit101 (where, for example, both share a common circuit board, chassis,power supply, and/or housing) or can be partially or wholly remote withrespect to the control circuit 101 (where, for example, the memory 102is physically located in another facility, metropolitan area, or evencountry as compared to the control circuit 101).

In addition to information regarding a plurality of pre-existing vettedradiation treatment plans for a variety of patients as described herein,this memory 102 can serve, for example, to non-transitorily store thecomputer instructions that, when executed by the control circuit 101,cause the control circuit 101 to behave as described herein. (As usedherein, this reference to “non-transitorily” will be understood to referto a non-ephemeral state for the stored contents (and hence excludeswhen the stored contents merely constitute signals or waves) rather thanvolatility of the storage media itself and hence includes bothnon-volatile memory (such as read-only memory (ROM) as well as volatilememory (such as an erasable programmable read-only memory (EPROM).)

The control circuit 101 may also optionally include a network interface.So configured the control circuit 101 can communicate with otherelements (both within the apparatus 100 and external thereto) via such anetwork interface. Network interfaces, including both wireless andnon-wireless platforms, are well understood in the art and require noparticular elaboration here.

By one optional approach the control circuit 101 operably couples to auser interface 103. This user interface 103 can comprise any of avariety of user-input mechanisms (such as, but not limited to, keyboardsand keypads, cursor-control devices, touch-sensitive displays,speech-recognition interfaces, gesture-recognition interfaces, and soforth) and/or user-output mechanisms (such as, but not limited to,visual displays, audio transducers, printers, and so forth) tofacilitate receiving information and/or instructions from a user and/orproviding information to a user.

FIG. 2 presents a process 200 that can be carried out, in whole or inpart, by the aforementioned control circuit 101. At block 201 thecontrol circuit 101 uses information to train at least one model. Inthis example the information comprises information 202 regarding aplurality of pre-existing vetted radiation treatment plans for a varietyof patients. This information can be accessed, for example, byretrieving the information from the aforementioned memory 102. As usedherein, the word “vetted” means that these radiation treatment planswere not only previously devised and optimized, but have also beenapproved by a relevant final authority for use and/or has actually beenutilized to administer a radiation treatment to the correspondingpatient. Accordingly, these plans do not represent mere academic orvirtual exercises but are plans that have seen real-world clinicalusage.

Generally speaking, these teachings anticipate extracting usefulinformation from those pre-existing vetted radiation treatment plansprior to and separately from the model training activities describedherein.

By one approach this process 200 will accommodate using pre-existingvetted radiation treatment plans having original formats that aredifferent from one another. In particular, the information 202 accessedby the control circuit 101 regarding these plans can comprise contentwhere the information regarding these pre-existing vetted radiationtreatment plans has a single consistent format.

By one approach, and for the sake of an illustrative example to be usedherein, the model comprises a dose volume histogram (DVH) estimationmodel.

DVH's typical represent three-dimensional dose distributions in agraphical two-dimensional format (the three-dimensional dosedistributions being created, for example, in a computerizedradiation-treatment planning system based on a three-dimensionalreconstruction of an X-ray computed tomography scan and study). The“volume” referred to in DVH analysis can be, for example, theradiation-treatment target, a healthy organ located near such a target,an arbitrary structure, and so forth.

DVH's are often visualized in either of two ways: as differential DVH'sor as cumulative DVH's. With differential DVH's column height for agiven dose bin corresponds to the volume of the structure that receivesthat dose. Bin doses typically extend along the horizontal axis whilestructure volumes (either percent or absolute volumes) extend along thevertical axis.

A cumulative DVH is typically plotted with bin doses along thehorizontal axis but has a column height for the first bin thatrepresents the volume of structure(s) that receive greater than or equalto that dose. The column height of the second bin then represents thevolume of structure(s) that receive greater than or equal to that dose,and so forth. With high granularity a cumulative DVH often appears as asmooth line graph. For many application settings cumulative DVH's arepreferred over differential DVH's but this process 200 can accommodateeither approach.

In this example the information used to train a DVH estimation model caninclude various parameters from exemplary patient cases. Examples ofsuch parameters include parameters calculated from planned dosedistribution matrices and parameters representing complex geometricrelationships in corresponding patients. As noted, these pre-existingvetted radiation treatment plans are for a corresponding variety ofpatients.

That said, this information 202 (such as the aforementioned parameters)can comprise an abridged version of the corresponding radiationtreatment plans. For example, by one approach the information comprises,at least to a large extent, only the aforementioned extractedparameters. Specific examples of such information can include biologicalstructure sets or descriptions, dose prescriptions, and field geometryinformation but not any information that can specifically identify aparticular patient such as names, images that present the person's face,or the like. The information is therefore anonymous and is not outwardlyintrinsically identifying of any of the patients. Accordingly, theprivacy of these patients is preserved. If desired, such anonymity makesit considerably easier to share such information from oneenterprise/institution to another.

It will also be appreciated that extracting these relevant parametersprior to providing the information to the control circuit 101 results ina considerably smaller data set than the original data set. This canspeed up the pre-training data acquisition process and can also resultin faster training of the model, hence potentially avoiding any need fora technologically upgraded or faster control circuit 101.

By one approach more than one DVH estimation model may be trained for aparticular treatment situation (each model typically being specific to acertain use rather than to only a certain patient). For example, aseparate model may be utilized for each organ at risk as well as thetarget volume. These teachings will also accommodate partitioning organsat risk into qualitatively different sub-volumes that may each havedifferent DVH estimation requirements.

The foregoing can be appropriate when the overlapping relationship of aparticular organ at risk with respect to a target volume changes withrespect to the treatment field. For example, in any given field, somepart of a particular organ at risk may overlap with the target volume(and hence be subject to a required target dose level), another portionof the organ at risk, while not overlapping with the target volume, maynevertheless be within the ambit of the active or passive beam-shapingaperture (and hence be subject either to modulated fluence or so-calledleaf-transmission fluence), and another portion of the organ at risk maybe external to the field (and hence be essentially protected fromfluence). (Fluence, of course, represents radiative flux integrated overtime and comprises a fundamental metric in dosimetry (i.e., themeasurement and calculation of an absorbed dose of ionizing radiation inmatter and tissue).)

The use of models in general and of DVH estimation models in particular,and the training of models, constitutes a generally well-understood areaof prior art endeavor. That said, and referring momentarily to FIG. 3 ,the training of the aforementioned DVH estimation model can benefit fromone or more optional approaches in these regards.

As a first example in these regards, at blocks 302 and 303 this activitycan include identifying outlier data in the information and thenavoiding relying upon that outlier data when training the at least onemodel. When training a new DVH estimation model based on existingclinical data, it can be useful to create the training set from acoherent set of treatment plans. That said, such training will rely oncomplex metrics calculated from the treatment plans. Examples includeorgan overlap, DVH curve points, and in-field metrics, to note but afew. Identifying potential outliers by simply reviewing and comparingtreatment plan parameters may be difficult in numerous real worldsettings.

These teachings contemplate visualizing various parameters used by a DVHestimation training algorithm that will allow a user to distinguishoutliers in the training set. Parameters and visualizations can include,for example, DVH graphs, illustrating the data sets used by a regressionanalysis including regression lines, and a particular DVH estimatedusing the model against every patient geometry in the training set.

These teachings will also accommodate using a single numerical index forevery training set plan comprised of various independent parameterscalculated for that plan using the training algorithm of choice. Using asingle outlier index can further aid the user to easily view thetraining set and distinguish outliers.

FIG. 4 presents some illustrative examples in these regards. Inparticular, data may constitute an outlier by virtue of being ageometric outlier 401, a dosimetric outlier 402, or an influence pointoutlier 403.

As another example, and as illustrated at block 304, the control circuit101 can be configured to use original prescription dose levels thatcorrespond to at least some of the pre-existing vetted radiationtreatment plans when training the model. For example, the controlcircuit 101 may use the original prescription dose levels whennormalizing dose volume histograms.

Normalization, of course, typically affects the general scaling ofcorresponding estimates. If the normalization assumption at the time ofestimation differs, for example, from what was used at the time oftraining the DVH estimation model, the estimates may be scaledsub-optimally.

Accordingly, the present teachings will accommodate obtaining the doselevel of target structures in a particular training case from acorresponding dose matrix. That said, it will not typically be clearfrom a dose distribution alone which normalization has been used in agiven instance. Accordingly, these teachings will accommodate the userproviding, in addition to the dose matrix, a nominal dose level for theplan. The used normalization can then be deduced by comparing the targetDVH and the user given dose level. The normalization information canalso be used for other dose level deductions during the process offorming individual plans.

More specifically, these teachings will accommodate providing the userthe opportunity to describe the intended dose level for each trainingset plan. This can comprise, for example, using the user-given doselevel together with the dose distribution in target structures to definethe user plan normalization during DVH estimation configuration. Asanother example, this can comprise using the determined plannormalization to scale other dose levels during DVH estimationconfiguration. And as yet another example, this can comprise using thedetermined plan normalization to back scale the user-given dose levelduring DVH estimation.

Referring again to FIG. 2 , at optional block 203 the control circuit101 can assess how well a particular patient fits the information 202used to train the aforementioned model. As one specific example in theseregards, the control circuit 101 conducts this assessment as a functionof geometric limits 204 that corresponds to the information used totrain the model. This approach can be particularly useful when the DVHestimation model contains information about the geometric limits (i.e.,statistics of the independent model parameters) of the training set usedfor the model training.

Such an assessment can be useful because regression models, like mostother models, are often valid only in a region spanned by the trainingset. For example, if all organ volumes of a certain organ at risk in aparticular model are less than 5 deciliters, it will be difficult forsuch a model to work well in an application setting where the organvolume is 7 deciliters.

Accordingly, and specifically, these teachings will support providing,during estimation of a DVH, information regarding how well a currentplan fits the training set used to train the DVH estimation model. Asnoted, this information can be statistics regarding independentgeometric parameters of the training set plans. That said, in lieu ofthe foregoing or in combination therewith this information can bestatistics of the dependent dose parameters of training set plans ascompared to estimated dose parameters.

This information can then be used during the creation of estimates asdescribed herein to check how well the current case is fitting to thetraining set.

When the control circuit 101 determines that the particular patient doesnot fit the information used to train the model within at least apredetermined range of suitability (determined, for example, at decisionblock 207), the control circuit 101 can present a user warning 208 via,for example, the aforementioned user interface 103.

At optional decision block 205 this process 200 can provide fordetermining a validation status of the aforementioned trained model. Forexample, the control circuit 101 can assess whether the model has beenreviewed and validated by an authorized user or group of users. When nottrue, these teachings will accommodate inhibiting availability of thatmodel (at block 206) unless and until the model is validated per thevalidation process of the user.

This capability can be particularly useful when models are sharedbetween different application settings (for example, at differenttreatment clinics). In this case, a particular model can be validated ata receiving clinic to ensure that the model appropriately matches alocal patient population and/or local treatment practices/platforms.

By one approach, availability of an un-validated model can be inhibitedby preventing publication of the model at issue (where “publication” canrefer to identifying information for the model being made available,internally or externally, to the relevant enterprise).

At block 209 the control circuit 101 uses the aforementioned trainedmodel to develop estimates for a radiation treatment plan for aparticular patient. There are various ways by which this can beaccomplished. By one approach, for example, the DVH estimation modelevaluates principal components of the area to be exposed forgeometry-based expected dose volume histograms to thereby developcorresponding geometric parameters. Regression model coefficients canthen be utilized to estimate corresponding DVH parameters using aregression model. The resultant DVH parameters can then be utilized toprovide resultant DVH estimates for the particular patient.

These teachings are highly flexible in these regards and willaccommodate various additional approaches. FIG. 5 provides someillustrative examples in these regards.

At optional block 501, this activity can comprise accessing metadata ascorresponds to the information used to train the model to help ensureselection of a suitable DVH estimation model for a particular patient.Useful examples of metadata include a model identifier, a shortdescription of the model, a long description of the model (for example,in the form of an electronic document such as a datasheet that mayinclude both text and non-textual graphics), and structured metadataassociated with the model describing such things as biologicalstructures, associated structure codes, and identification of thetreatment site (such as the pelvis, thorax, and so forth).

At optional block 502, then, the control circuit can use that metadatato select a particular model to use when developing the estimates forthe radiation treatment plan for the particular patient. For example,selection of a particular model can be based on structure definitionsthat are compared and automatically matched between the patient case andthe DVH estimation model. So configured, selection of a particular modelcan be based on free-form textual information that contains clinicallyrelevant description of the model's intended use.

By one approach, these teachings will also accommodate using suchinformation to permit the control circuit to automatically propose amost suitable model for a particular patient as selected from amongst aplurality of available, validated models.

At optional block 503, this activity can comprise determining at leastone radiation treatment plan objective for use when developing anoptimized radiation treatment plan for the aforementioned particularpatient. By one approach, the control circuit 101 determines thisradiation treatment plan objective, at least in part, by forming theobjective based upon dose volume histogram estimates 504 that wereformed using the aforementioned model.

Clinical evaluation of radiation treatment plan quality often treatscertain regions of a DVH as being more important than other areas.Specific priorities for different regions can depend on which organ atrisk is being considered, the treatment modality, and other factors ofchoice. These teachings will also accommodate, if desired, providing auser opportunity (for example, via the aforementioned user interface103) to modify the aforementioned radiation treatment plan objective andusing that user input when determining this radiation treatment planobjective or objectives. For example, the user can have the opportunityto instruct the DVH estimation process to emphasize certain regionsduring the objective generation process.

By way of illustration, the configuration of a DVH estimation model canbe leveraged by a user to deduce various dynamic objectives (that is,objectives whose exact location is deduced at the same time as are theDVH estimates). A user configuring a DVH estimation model can use thesedifferent objectives to provide guidance as regards the clinicallyimportant features in the DVH information.

Examples of dynamic objective include, but are not limited to, a pointobjective with a user-defined dose level (absolute or relative withrespect to a prescription) where volume of the objective is dynamic, apoint objective with a user-defined volume, a mean objective without aspecified dose value, and a gEUD objective without a specified dosevalue. In all of these cases the user can have control with respect tosome features of the generated objective while other features arededuced based on estimates. (In the case of a line objective thatfollows an estimated range other than in regions where the targetoverlaps with an organ at risk, the line objective can follow the targetdose level if desired.)

Generally speaking, these teachings will accommodate generatingobjectives partly based on user-provided information and partly based onDVH estimations. More specifically, these teachings will accommodate:

-   -   generating a point objective where a user defines a desired dose        level while volume parameters and priority are defined based on        DVH estimates;    -   generating a point objective where the user defines a desired        volume level while a dose parameter and priority are defined        based on DVH estimates;    -   generating a mean-dose objective where a dose parameter and        priority is defined based on DVH estimates;    -   generating a gEUD objective where the user defines the alpha        parameter while dose parameter and priority are defined based on        DVH estimates; and    -   generating any of the foregoing where the user also defines the        corresponding priority.

These teachings will also accommodate, as another illustrative examplein these regards, generating line objectives while automaticallyhandling overlap-to-target regions and transforming the objective lineso that the latter does not conflict significantly with targetobjectives.

At optional block 210 this process 200 can use the aforementionedestimates for a radiation treatment plan to develop, in turn, acandidate radiation treatment plan for the particular patient. The useof input information to develop a radiation treatment plan comprises awell-understood area of prior art endeavor and requires no furtherelaboration here.

And at optional block 211 this process 200 can serve to display theaforementioned candidate radiation treatment plan via, for example, theaforementioned user interface 103.

DVH estimates (or, more generally, dose estimates) reflect achievabledosing for a defined prescription (often comprising target dosedelineation and target dose level). Often these estimates are comparedto a plan that is intended to be delivered to evaluate the quality ofthe plan. Unfortunately, changes in these regards can occur. Forexample, the intended plan may be re-normalized to a different doselevel. (For example, this can occur when a treatment is interrupted anda remaining portion of the treatment is then re-planned.) When thetarget dose levels do not match, the resultant estimation for an organat risk can be unacceptably high.

By one approach, the aforementioned display of information can include asimultaneous presentation of an overall target dose level thatcorresponds to the candidate radiation treatment plan. (By one approachthe estimation target dose level is always displayed in combination withthe prescribed dose level.) These teachings will accommodate, forexample, presenting the dose estimation as a DVH and/or as a dosematrix. So configured, this approach can help a user confirm the lack ofany mismatch between the dose level used in the estimation and theprescribed dose level in the intended plan.

The radiation-treatment platform 104 can then receive and utilize anapproved radiation treatment plan as developed per the foregoing. Inparticular, the radiation-treatment platform 104 can employ thespecifics of the approved radiation treatment plan when administering aradiation treatment to a particular patient.

Those skilled in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

What is claimed is:
 1. An apparatus comprising: a memory having storedtherein information regarding a plurality of pre-existing vettedradiation treatment plans for a variety of patients, wherein theinformation regarding the plurality of pre-existing vetted radiationtreatment plans for a variety of patients includes, at least in part, anabridged version of at least some of the pre-existing vetted radiationtreatment plans such that the abridged version is anonymous; a controlcircuit operably coupled to the memory and configured to: use theinformation to train at least one model; use the model to developestimates for a radiation treatment plan for a particular patient. 2.The apparatus of claim 1 wherein the information regarding the pluralityof pre-existing vetted radiation treatment plans for a variety ofpatients constitutes an abridged version of each of the pre-existingvetted radiation treatment plans such that the information is anonymousand not intrinsically correlated to any of the patients.
 3. Theapparatus of claim 1 wherein at least some of the plurality ofpre-existing vetted radiation treatment plans have original formats thatare different from one another and wherein the information regarding theplurality of pre-existing vetted radiation treatment plans presentscontents of the pre-existing vetted radiation treatment plans in asingle consistent format.
 4. The apparatus of claim 1 wherein thecontrol circuit is configured to use the information to train the atleast one model by, at least in part: identifying outlier data in theinformation; and disregarding the outlier data when training the atleast one model.
 5. The apparatus of claim 4 wherein the control circuitis configured to identify the outlier data in the information as afunction, at least in part, of at least one of: dose volume histogramgraphs; a regression analysis; and a particular dose volume histogramthat is estimated by comparing the model against every patient geometryin the information.
 6. The apparatus of claim 4 wherein the controlcircuit is configured to identify the outlier data in the information asa function, at least in part, of a single numerical index for each ofthe pre-existing vetted radiation treatment plans.
 7. The apparatus ofclaim 4 wherein the control circuit is configured to identify theoutlier data in the information as a function, at least in part, ofparticular data comprising at least one of a geometric outlier, adosimetric outlier, and an influence point outlier.
 8. The apparatus ofclaim 1 wherein the control circuit is configured to use the informationto train at least one model by, at least in part, using originalprescription dose levels as correspond to at least some of thepre-existing vetted radiation treatment plans when training the at leastone model.
 9. The apparatus of claim 8 wherein the control circuit isconfigured to use the original prescription dose levels when trainingthe at least one model by, at least in part, using the originalprescription dose levels when normalizing dose volume histograms. 10.The apparatus of claim 1 wherein the control circuit is furtherconfigured to: select the model to use to develop the estimates for theradiation treatment plan for the particular patient.
 11. The apparatusof claim 10 wherein the control circuit is configured to select themodel to use to develop the estimates for the radiation treatment planby, at least in part, using metadata as corresponds to the informationused to train the model.
 12. The apparatus of claim 1 wherein thecontrol circuit is further configured to: use geometric limits ascorrespond to the information used to train the model to assess how wellthe particular patient fits the information used to train the model. 13.The apparatus of claim 12 wherein the control circuit is furtherconfigured to: present a user warning upon determining that theparticular patient does not fit the information used to train the modelwithin at least a predetermined range of suitability.
 14. The apparatusof claim 1 wherein the control circuit is further configured to:determine at least one radiation treatment plan objective for theradiation treatment plan for the particular patient.
 15. The apparatusof claim 14 wherein the control circuit is configured to determine theat least one radiation treatment plan objective, at least in part, byforming the at least one objective based upon dose volume histogramestimates formed using the model.
 16. The apparatus of claim 15 whereinforming the at least one objective based upon dose volume histogramestimates formed using the model includes emphasizing at least onesub-region, but not all, of a corresponding dose volume histogram duringan objective generation process.
 17. The apparatus of claim 15 whereinthe control circuit is further configured to: provide a user opportunityto modify the at least one radiation treatment plan objective for theradiation treatment plan for the particular patient.
 18. The apparatusof claim 1 wherein the control circuit is further configured to:determine a validation status of the at least one model; inhibitavailability of the at least one model unless the validation status ofthe at least one model has at least a predetermined value.
 19. Theapparatus of claim 18 wherein the control circuit is configured toinhibit availability of the at least one model unless the validationstatus of the at least one model has at least a predetermined value by,at least in part, preventing publication of the at least one model. 20.The apparatus of claim 1 wherein the control circuit is furtherconfigured to: use the estimates for a radiation treatment plan todevelop a candidate radiation treatment plan; when displaying thecandidate radiation treatment plan, also simultaneously displaying anoverall target dose level as corresponds to the candidate radiationtreatment plan.